Impact of coronary atherosclerosis progression on cardiovascular events in patients with suspected coronary artery disease: A two-center retrospective analysis of 1062 cases stratified by age.

Prognostic value of coronary CT angiography for the prediction of all-cause mortality and non-fatal myocardial infarction: a propensity score analysis.

To explore the relationship between comprehensive assessment of coronary atherosclerosis by coronary CT angiography (CCTA) and all-cause mortality and non-fatal myocardial infarction in the Chinese population. Sixty-three patients from the prospective long-term study who experienced major adverse cardiovascular events (MACE) during the follow-up were included. No-MACE patients were 1:1 propensity-matched. Various qualitative and quantitative CCTA parameters, such as coronary artery calcium score (CACS), high-risk plaque, coronary artery disease (CAD) severity, number of obstructive vessels, segment involvement score (SIS), segment stenosis score (SSS), computed tomography-adapt Leaman score (CT-LeSc), and peri-coronary adipose tissue (PCAT) CT attenuation, were compared between both groups. Cox regression analysis was performed to determine the association between CCTA parameters and MACE. The MACE group had higher CACS, more high-risk plaques, more obstructive CAD, more obstructive vessels, higher PCAT CT attenuation, and higher coronary atherosclerotic burden (SIS: 5.76 ± 3.36 vs. 2.84 ± 3.07; SSS: 11.06 ± 8.41 vs. 3.94 ± 4.78; CT-LeSc: 11.25 ± 6.57 vs. 5.49 ± 5.82) than the control group (all p < 0.05). On multivariable analysis, hazard ratios were 1.058 for the SSS (p = 0.004), and 2.152 for the obstructive CAD. When the burden of coronary atherosclerosis was defined as the CT-LeSc, hazard ratios were 1.057 for the CT-LeSc (p = 0.036), and 2.272 for the obstructive CAD. The SSS, CT-LeSc, and presence of obstructive CAD were independently associated with the all-cause mortality and non-fatal myocardial infarction in the suspected CADs in the Chinese population.

Identification of ischemia-causing lesions using coronary plaque quantification and changes in fractional flow reserve derived from computed tomography across the lesion.

Background:This study sought to evaluate the association between coronary plaque characteristics, changes in the fractional flow reserve (FFR) derived from computed tomography across the lesion (ΔFFRCT), and lesion-specific ischemia using the FFR in patients with suspected or known coronary artery disease.Methods:The study assessed coronary computed tomography (CT) angiography stenosis, plaque characteristics, ΔFFRCT, and FFR in 164 vessels of 144 patients. Obstructive stenosis was defined as stenosis ≥50%. An area under the receiver -operating characteristics curve (AUC) analysis was conducted to define the optimal thresholds for ΔFFRCT and the plaque variables. Ischemia was defined as a FFR of ≤0.80.Results:The optimal cut-off value of ΔFFRCT was 0.14. Low-attenuation plaque (LAP) ≥76.23 mm3 and a percentage aggregate plaque volume (%APV) ≥28.91% can be used to predict ischemia independent of other plaque characteristics. The addition of LAP ≥76.23 mm3 and %APV ≥28.91% improved the discrimination (AUC, 0.742 vs. 0.649, P=0.001) and reclassification abilities [category-free net reclassification index (NRI), 0.339, P=0.027; relative integrated discrimination improvement (IDI) index, 0.093, P<0.001] of the assessments compared to the stenosis evaluation alone, and the addition of information about ΔFFRCT ≥0.14 further increased the discrimination (AUC, 0.828 vs. 0.742, P=0.004) and reclassification abilities (NRI, 1.029, P<0.001; relative IDI, 0.140, P<0.001) of the assessments.Conclusions:The addition of the plaque assessment and ΔFFRCT to the stenosis assessments improved the identification of ischemia compared to the stenosis assessment alone.

Bioinformatics analysis of potential common pathogenic mechanisms for systemic lupus erythematosus and acute myocardial infarction.

BACKGROUND:Systemic lupus erythematosus (SLE) patients have a higher risk of acute myocardial infarction (AMI) compared to the general population. However, the underlying common mechanism of this association is not fully understood. This study aims to investigate the molecular mechanism of this complication.METHODS:Gene expression profiles of SLE (GSE50772) and AMI (GSE66360) were obtained from the Gene Expression Omnibus (GEO) database. Common differentially expressed genes (DEGs) in SLE and AMI were identified, and functional annotation, protein-protein interaction (PPI) network analysis, module construction, and hub gene identification were performed. Additionally, transcription factor (TF)-gene regulatory network and TF-miRNA regulatory network were constructed for the hub genes.RESULTS:70 common DEGs (7 downregulated genes and 63 upregulated genes) were identified and were mostly enriched in signaling pathways such as the IL-17 signaling pathway, TNF signaling pathway, lipid metabolism, and atherosclerosis. Using cytoHubba, 12 significant hub genes were identified, including IL1B, TNF, FOS, CXCL8, JUN, PTGS2, FN1, EGR1, CXCL1, DUSP1, MMP9, and ZFP36.CONCLUSIONS:This study reveals a common pathogenesis of SLE and AMI and provides new perspectives for further mechanism research. The identified common pathways and hub genes may have important clinical implications for the prevention and treatment of AMI in SLE patients.

[Differential diagnosis between pulmonary artery sarcoma and central chronic pulmonary thromboembolism:a preliminary study on CT signs].

Objective: To improve the diagnostic accuracy of pulmonary artery sarcoma, and to distinguish it from central chronic pulmonary thromboembolism using CT scans. Methods: In this retrospective study, two groups of pulmonary artery sarcoma (PAS group) and central chronic pulmonary thromboembolism (central CPTE group) confirmed by pathology at our hospital between August 2009 and July 2019 were enrolled, clinical features and pre-operative CT pulmonary artery manifestation were collected, and the key points of differential diagnosis were summarized. Results: The study was composed of 13 cases in the PAS group including 10 males (76.9%), with an average age of (45.4±15.5) years. There were 19 patients in the central CPTE group including 14 males (73.7%), with an average age of (38.6±14.1) years. There were no significant differences in gender and age between the two groups. Deep venous thrombosis in the lower extremities was significantly higher in the central CPTE group than in the PAS group (7/19 vs. 0/13, P=0.025), and the N-terminal pro-brain natriuretic peptide value was higher in the central CPTE group than in the PAS group [674.50(261.70-1 977.70) vs. 66.00(28.10-505.50),P=0.001]. In CT pulmonary angiography, the involvement of the main pulmonary artery, and the proximal lesion showing an acute angle to the pulmonary artery wall were more common in the PAS group [11(84.6%) vs. 5(26.3%), P=0.003; 11(84.6%) vs. 2(10.5%), P<0.001, respectively]. The swelling index of the main pulmonary and the left/right main pulmonary arteries in the PAS group were significantly higher, as well as the dilatation in the lobar and segmental pulmonary arteries [1.19±0.17 vs. 0.99±0.19,P=0.006, 10(76.9%) vs. 2(10.5%), P<0.001, respectively]. The right ventricular transverse diameter/left ventricular transverse diameter (RVd/LVd) and pulmonary artery diameter/ascending aortic diameter ratio (Pad/Aod) were significantly lower in PAS group than those in the central CPTE group (0.97±0.19 vs. 1.23±0.35,P=0.020; 0.98±0.25 vs. 1.15±0.20,P=0.039). Conclusions: In CT pulmonary angiography, filling defects involving the main pulmonary artery and showing expansive growth were highly suggestive of pulmonary artery sarcoma. The history of deep venous thrombosis of the lower extremities was helpful for the diagnosis of chronic pulmonary embolism.

Pericoronary fat attenuation index and coronary plaque quantified from coronary computed tomography angiography identify ischemia-causing lesions.

BACKGROUND:The association between pericoronary fat attenuation index (FAI), plaque characteristics, and lesion-specific ischemia identified by fractional flow reserve (FFR) remains unclear.METHODS:Coronary computed tomography angiography (CCTA) stenosis, FAI, plaque characteristics, FFR derived from computed tomography (FFRCT) and FFR were assessed in 280 vessels of 247 patients. Stenosis ≥50% was considered obstructive. Optimal thresholds of FAI and plaque variables were defined by the area under the receiver-operating characteristics curve (AUC) analysis. Ischemia was defined by FFR ≤ 0.80.RESULTS:FAI ≥ -71.9 HU, low-attenuation plaque (LAP) ≥ 49.62 mm3 and aggregate plaque volume (APV) ≥ 28.91% predicted ischemia independent of other plaque characteristics. The addition of FAI ≥ -71.9 HU improved discrimination (AUC, 0.720 vs. 0.674, P = 0.035) and reclassification abilities (category-free net reclassification index [NRI], 0.470, P < 0.001; relative integrated discrimination improvement [IDI], 0.047, P < 0.001) of ischemia compared with stenosis evaluation alone, with further discrimination (AUC, 0.772 vs. 0.720, P = 0.028) and reclassification abilities (NRI, 0.385, P = 0.001; relative IDI, 0.077, P < 0.001) of ischemia by adding information regarding LAP ≥49.62 mm3 + APV ≥ 28.91%. And the diagnostic performance of combination approach was comparable to that of FFRCT alone (AUC, 0.772 vs. 0.762, P = 0.771).CONCLUSIONS:Stenosis severity, FAI, plaque characteristics predicted lesion-specific ischemia. The combination of FAI and plaque assessment improved the discrimination of ischemia compared with stenosis assessment alone.

Identification of pathology-confirmed vulnerable atherosclerotic lesions by coronary computed tomography angiography using radiomics analysis.

OBJECTIVES:To explore whether radiomics-based machine learning (ML) models could outperform conventional diagnostic methods at identifying vulnerable lesions on coronary computed tomographic angiography (CCTA).METHODS:In this retrospective study, 36 heart transplant recipients with coronary heart disease (CAD) and end-stage heart failure were included. Pathological cross-section samples of 350 plaques were collected and coregistered to patients' preoperative CCTA images. A total of 1184 radiomic features were extracted from CCTA images. Through feature selection and stratified fivefold cross-validation, we derived eight radiomics-based ML models for lesion vulnerability prediction. An independent set of 196 plaques from another 8 CAD patients who underwent heart transplants was collected to validate radiomics-based ML models' diagnostic accuracy against conventional CCTA feature-based diagnosis (presence of at least 2 high-risk plaque features). The performance of the prediction models was assessed by the area under the receiver operating characteristic curve (AUC) with 95% confidence intervals (CI).RESULTS:The training group used to develop radiomics-based ML models contained 200/350 (57.1%) vulnerable plaques and the external validation group was composed of 67.3% (132/196) vulnerable plaques. The radiomics-based ML model based on eight radiomic features showed excellent cross-validation diagnostic accuracy (AUC: 0.900 ± 0.033). In the validation group, diagnosis based on conventional CCTA features demonstrated moderate performance (AUC: 0.656 [95% CI: 0.593 -0.718]), while the radiomics-based ML model showed higher diagnostic ability (0.782 [95% CI: 0.710 -0.846]).CONCLUSIONS:Radiomics-based ML models showed better diagnostic ability than the conventional CCTA features at assessing coronary plaque vulnerability.KEY POINTS:• CCTA has great potential in the diagnosis of vulnerable coronary artery lesions. • Radiomics model built through CCTA could discriminate coronary vulnerable lesions in good diagnostic ability. • Radiomics model could improve the ability of vulnerability diagnosis against traditional CCTA method, sensitivity especially.

Dose Reduction of Dynamic Computed Tomography Myocardial Perfusion Imaging by Tube Voltage Change: Investigation in a Swine Model.

Background:It is unclear whether tube voltage influences the measurement of perfusion parameters. The present study sought to evaluate the influence of tube voltage change on myocardial blood flow (MBF) measurements in dynamic computed tomography myocardial perfusion imaging (CTP).Methods and Results:Seven swine [mean weight 55.8 kg ± 1.6 (standard deviation)] underwent rest and stress dynamic CTP with tube voltages of 100 and 70 kV. The image noise, signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR), radiation dose and MBF value were compared. The 70 kV images had higher CT attenuation and higher image noise (27.9 ± 2.4 vs. 21.5 ± 1.9, P < 0.001) than the 100 kV images, resulting in a higher SNR (20.5 ± 1.6 vs. 15.6 ± 1.8, P < 0.001) and CNR (17.6 ± 1.5 vs. 12.4 ± 1.7, P < 0.001). Compared to the use of conventional 100 kV, 70 kV yielded an approximately 64.6% radiation dose reduction while generating comparable MBF values, both at rest (88.3 ± 14.9 ml/100 g/min vs. 85.6 ± 17.4 ml/100 g/min, P = 0.21) and stress (101.4 ± 21.5 ml/100 g/min vs. 99.6 ± 21.4 ml/100 g/min, P = 0.58) states.Conclusion:Dynamic CTP using 70 kV instead of 100 kV does not substantially influence the MBF value but significantly reduces the radiation dose. Additional research is required to investigate the clinical significance of this change.

The Perivascular Fat Attenuation Index Improves the Diagnostic Performance for Functional Coronary Stenosis.

Background: Coronary computed tomography angiography (CCTA) is an established first-line test in the investigation of patients with suspected coronary artery disease (CAD), while the perivascular fat attenuation index (FAI) derived from CT seems to be a feasible and efficient tool for the identification of ischemia. The association between the FAI and lesion-specific ischemia as assessed by fractional flow reserve (FFR) remains unclear. Methods: In a total of 261 patients, 294 vessels were assessed for CCTA stenosis, vessel-specific FAI, lesion-specific FAI, and plaque characteristics. The diagnostic accuracies of each parameter and the combined approach were analyzed via the receiver operating characteristic curve (ROC) with FFR as the reference standard. The determinants of FAI were statistically analyzed. Results: The cutoff values of vessel-specific FAI and lesion-specific FAI scores calculated according to the Youden index were −70.97 and −73.95 HU, respectively. No significant differences were noted between them; however, they exhibited a strong correlation. No significant differences were noted between the area under the curve (AUC) scores of vessel-specific FAI (0.677), lesion-specific FAI (0.665), and CCTA (0.607) (p > 0.05 for all) results. The addition of two FAI measures to the CCTA showed improvements in the discrimination (AUC) and reclassification ability (relative integrated discrimination improvement (IDI) and category-free net reclassification index (NRI)), vessel-specific FAI (AUC, 0.696; NRI, 49.6%; IDI, 5.9%), and lesion-specific FAI scores (AUC, 0.676; NRI, 43.3%; IDI, 5.4%); (p < 0.01 for all). Multivariate analysis revealed that low-attenuation plaque (LAP) volume was an independent predictor of two FAI measures. Conclusion: The combined approach of adding vessel-specific FAI or lesion-specific FAI scores could improve the identification of ischemia compared with CCTA alone. The LAP volume was the independent risk factor for both tools.

Diagnostic Performance of CT FFR With a New Parameter Optimized Computational Fluid Dynamics Algorithm From the CT-FFR-CHINA Trial: Characteristic Analysis of Gray Zone Lesions and Misdiagnosed Lesions.

To assess the diagnostic performance of fractional flow reserve (FFR) derived from coronary computed tomography angiography (CTA) (CT-FFR) obtained by a new computational fluid dynamics (CFD) algorithm to detect ischemia, using FFR as a reference, and analyze the characteristics of "gray zone" and misdiagnosed lesions. This prospective multicenter clinical trial (NCT03692936, https://clinicaltrials.gov/) analyzed 317 patients with coronary stenosis between 30 and 90% in 366 vessels from five centers undergoing CTA and FFR between November 2018 and March 2020. CT-FFR were obtained from a CFD algorithm (Heartcentury Co., Ltd., Beijing, China). Diagnostic performance of CT-FFR and CTA in detecting ischemia was assessed. Coronary atherosclerosis characteristics of gray zone and misdiagnosed lesions were analyzed. Per-vessel sensitivity, specificity and accuracy for CT-FFR and CTA were 89.9, 87.8, 88.8% and 89.3, 35.5, 60.4%, respectively. Accuracy of CT-FFR was 80.0% in gray zone lesions. In gray zone lesions, lumen area and diameter were significantly larger than lesions with FFR < 0.76 (both p < 0.001), lesion length, non-calcified and calcified plaque volume were all significantly higher than non-ischemic lesions (all p < 0.05). In gray zone lesions, Agatston score (OR = 1.009, p = 0.044) was the risk factor of false negative results of CT-FFR. In non-ischemia lesions, coronary stenosis >50% (OR = 2.684, p = 0.03) was the risk factor of false positive results. Lumen area (OR = 0.567, p = 0.02) and diameter (OR = 0.296, p = 0.03) had a significant negative effect on the risk of false positive results of CT-FFR. In conclusion, CT-FFR based on the new parameter-optimized CFD model provides better diagnostic performance for lesion-specific ischemia than CTA. For gray zone lesions, stenosis degree was less than those with FFR < 0.76, and plaque load was heavier than non-ischemic lesions.

Impact of coronary atherosclerosis progression on cardiovascular events in patients with suspected coronary artery disease: A two-center retrospective analysis of 1062 cases stratified by age.

Prognostic value of coronary CT angiography for the prediction of all-cause mortality and non-fatal myocardial infarction: a propensity score analysis.

To explore the relationship between comprehensive assessment of coronary atherosclerosis by coronary CT angiography (CCTA) and all-cause mortality and non-fatal myocardial infarction in the Chinese population. Sixty-three patients from the prospective long-term study who experienced major adverse cardiovascular events (MACE) during the follow-up were included. No-MACE patients were 1:1 propensity-matched. Various qualitative and quantitative CCTA parameters, such as coronary artery calcium score (CACS), high-risk plaque, coronary artery disease (CAD) severity, number of obstructive vessels, segment involvement score (SIS), segment stenosis score (SSS), computed tomography-adapt Leaman score (CT-LeSc), and peri-coronary adipose tissue (PCAT) CT attenuation, were compared between both groups. Cox regression analysis was performed to determine the association between CCTA parameters and MACE. The MACE group had higher CACS, more high-risk plaques, more obstructive CAD, more obstructive vessels, higher PCAT CT attenuation, and higher coronary atherosclerotic burden (SIS: 5.76 ± 3.36 vs. 2.84 ± 3.07; SSS: 11.06 ± 8.41 vs. 3.94 ± 4.78; CT-LeSc: 11.25 ± 6.57 vs. 5.49 ± 5.82) than the control group (all p < 0.05). On multivariable analysis, hazard ratios were 1.058 for the SSS (p = 0.004), and 2.152 for the obstructive CAD. When the burden of coronary atherosclerosis was defined as the CT-LeSc, hazard ratios were 1.057 for the CT-LeSc (p = 0.036), and 2.272 for the obstructive CAD. The SSS, CT-LeSc, and presence of obstructive CAD were independently associated with the all-cause mortality and non-fatal myocardial infarction in the suspected CADs in the Chinese population.

Identification of ischemia-causing lesions using coronary plaque quantification and changes in fractional flow reserve derived from computed tomography across the lesion.

Background:This study sought to evaluate the association between coronary plaque characteristics, changes in the fractional flow reserve (FFR) derived from computed tomography across the lesion (ΔFFRCT), and lesion-specific ischemia using the FFR in patients with suspected or known coronary artery disease.Methods:The study assessed coronary computed tomography (CT) angiography stenosis, plaque characteristics, ΔFFRCT, and FFR in 164 vessels of 144 patients. Obstructive stenosis was defined as stenosis ≥50%. An area under the receiver -operating characteristics curve (AUC) analysis was conducted to define the optimal thresholds for ΔFFRCT and the plaque variables. Ischemia was defined as a FFR of ≤0.80.Results:The optimal cut-off value of ΔFFRCT was 0.14. Low-attenuation plaque (LAP) ≥76.23 mm3 and a percentage aggregate plaque volume (%APV) ≥28.91% can be used to predict ischemia independent of other plaque characteristics. The addition of LAP ≥76.23 mm3 and %APV ≥28.91% improved the discrimination (AUC, 0.742 vs. 0.649, P=0.001) and reclassification abilities [category-free net reclassification index (NRI), 0.339, P=0.027; relative integrated discrimination improvement (IDI) index, 0.093, P<0.001] of the assessments compared to the stenosis evaluation alone, and the addition of information about ΔFFRCT ≥0.14 further increased the discrimination (AUC, 0.828 vs. 0.742, P=0.004) and reclassification abilities (NRI, 1.029, P<0.001; relative IDI, 0.140, P<0.001) of the assessments.Conclusions:The addition of the plaque assessment and ΔFFRCT to the stenosis assessments improved the identification of ischemia compared to the stenosis assessment alone.

Bioinformatics analysis of potential common pathogenic mechanisms for systemic lupus erythematosus and acute myocardial infarction.

BACKGROUND:Systemic lupus erythematosus (SLE) patients have a higher risk of acute myocardial infarction (AMI) compared to the general population. However, the underlying common mechanism of this association is not fully understood. This study aims to investigate the molecular mechanism of this complication.METHODS:Gene expression profiles of SLE (GSE50772) and AMI (GSE66360) were obtained from the Gene Expression Omnibus (GEO) database. Common differentially expressed genes (DEGs) in SLE and AMI were identified, and functional annotation, protein-protein interaction (PPI) network analysis, module construction, and hub gene identification were performed. Additionally, transcription factor (TF)-gene regulatory network and TF-miRNA regulatory network were constructed for the hub genes.RESULTS:70 common DEGs (7 downregulated genes and 63 upregulated genes) were identified and were mostly enriched in signaling pathways such as the IL-17 signaling pathway, TNF signaling pathway, lipid metabolism, and atherosclerosis. Using cytoHubba, 12 significant hub genes were identified, including IL1B, TNF, FOS, CXCL8, JUN, PTGS2, FN1, EGR1, CXCL1, DUSP1, MMP9, and ZFP36.CONCLUSIONS:This study reveals a common pathogenesis of SLE and AMI and provides new perspectives for further mechanism research. The identified common pathways and hub genes may have important clinical implications for the prevention and treatment of AMI in SLE patients.

[Differential diagnosis between pulmonary artery sarcoma and central chronic pulmonary thromboembolism:a preliminary study on CT signs].

Objective: To improve the diagnostic accuracy of pulmonary artery sarcoma, and to distinguish it from central chronic pulmonary thromboembolism using CT scans. Methods: In this retrospective study, two groups of pulmonary artery sarcoma (PAS group) and central chronic pulmonary thromboembolism (central CPTE group) confirmed by pathology at our hospital between August 2009 and July 2019 were enrolled, clinical features and pre-operative CT pulmonary artery manifestation were collected, and the key points of differential diagnosis were summarized. Results: The study was composed of 13 cases in the PAS group including 10 males (76.9%), with an average age of (45.4±15.5) years. There were 19 patients in the central CPTE group including 14 males (73.7%), with an average age of (38.6±14.1) years. There were no significant differences in gender and age between the two groups. Deep venous thrombosis in the lower extremities was significantly higher in the central CPTE group than in the PAS group (7/19 vs. 0/13, P=0.025), and the N-terminal pro-brain natriuretic peptide value was higher in the central CPTE group than in the PAS group [674.50(261.70-1 977.70) vs. 66.00(28.10-505.50),P=0.001]. In CT pulmonary angiography, the involvement of the main pulmonary artery, and the proximal lesion showing an acute angle to the pulmonary artery wall were more common in the PAS group [11(84.6%) vs. 5(26.3%), P=0.003; 11(84.6%) vs. 2(10.5%), P<0.001, respectively]. The swelling index of the main pulmonary and the left/right main pulmonary arteries in the PAS group were significantly higher, as well as the dilatation in the lobar and segmental pulmonary arteries [1.19±0.17 vs. 0.99±0.19,P=0.006, 10(76.9%) vs. 2(10.5%), P<0.001, respectively]. The right ventricular transverse diameter/left ventricular transverse diameter (RVd/LVd) and pulmonary artery diameter/ascending aortic diameter ratio (Pad/Aod) were significantly lower in PAS group than those in the central CPTE group (0.97±0.19 vs. 1.23±0.35,P=0.020; 0.98±0.25 vs. 1.15±0.20,P=0.039). Conclusions: In CT pulmonary angiography, filling defects involving the main pulmonary artery and showing expansive growth were highly suggestive of pulmonary artery sarcoma. The history of deep venous thrombosis of the lower extremities was helpful for the diagnosis of chronic pulmonary embolism.

Pericoronary fat attenuation index and coronary plaque quantified from coronary computed tomography angiography identify ischemia-causing lesions.

BACKGROUND:The association between pericoronary fat attenuation index (FAI), plaque characteristics, and lesion-specific ischemia identified by fractional flow reserve (FFR) remains unclear.METHODS:Coronary computed tomography angiography (CCTA) stenosis, FAI, plaque characteristics, FFR derived from computed tomography (FFRCT) and FFR were assessed in 280 vessels of 247 patients. Stenosis ≥50% was considered obstructive. Optimal thresholds of FAI and plaque variables were defined by the area under the receiver-operating characteristics curve (AUC) analysis. Ischemia was defined by FFR ≤ 0.80.RESULTS:FAI ≥ -71.9 HU, low-attenuation plaque (LAP) ≥ 49.62 mm3 and aggregate plaque volume (APV) ≥ 28.91% predicted ischemia independent of other plaque characteristics. The addition of FAI ≥ -71.9 HU improved discrimination (AUC, 0.720 vs. 0.674, P = 0.035) and reclassification abilities (category-free net reclassification index [NRI], 0.470, P < 0.001; relative integrated discrimination improvement [IDI], 0.047, P < 0.001) of ischemia compared with stenosis evaluation alone, with further discrimination (AUC, 0.772 vs. 0.720, P = 0.028) and reclassification abilities (NRI, 0.385, P = 0.001; relative IDI, 0.077, P < 0.001) of ischemia by adding information regarding LAP ≥49.62 mm3 + APV ≥ 28.91%. And the diagnostic performance of combination approach was comparable to that of FFRCT alone (AUC, 0.772 vs. 0.762, P = 0.771).CONCLUSIONS:Stenosis severity, FAI, plaque characteristics predicted lesion-specific ischemia. The combination of FAI and plaque assessment improved the discrimination of ischemia compared with stenosis assessment alone.

Identification of pathology-confirmed vulnerable atherosclerotic lesions by coronary computed tomography angiography using radiomics analysis.

OBJECTIVES:To explore whether radiomics-based machine learning (ML) models could outperform conventional diagnostic methods at identifying vulnerable lesions on coronary computed tomographic angiography (CCTA).METHODS:In this retrospective study, 36 heart transplant recipients with coronary heart disease (CAD) and end-stage heart failure were included. Pathological cross-section samples of 350 plaques were collected and coregistered to patients' preoperative CCTA images. A total of 1184 radiomic features were extracted from CCTA images. Through feature selection and stratified fivefold cross-validation, we derived eight radiomics-based ML models for lesion vulnerability prediction. An independent set of 196 plaques from another 8 CAD patients who underwent heart transplants was collected to validate radiomics-based ML models' diagnostic accuracy against conventional CCTA feature-based diagnosis (presence of at least 2 high-risk plaque features). The performance of the prediction models was assessed by the area under the receiver operating characteristic curve (AUC) with 95% confidence intervals (CI).RESULTS:The training group used to develop radiomics-based ML models contained 200/350 (57.1%) vulnerable plaques and the external validation group was composed of 67.3% (132/196) vulnerable plaques. The radiomics-based ML model based on eight radiomic features showed excellent cross-validation diagnostic accuracy (AUC: 0.900 ± 0.033). In the validation group, diagnosis based on conventional CCTA features demonstrated moderate performance (AUC: 0.656 [95% CI: 0.593 -0.718]), while the radiomics-based ML model showed higher diagnostic ability (0.782 [95% CI: 0.710 -0.846]).CONCLUSIONS:Radiomics-based ML models showed better diagnostic ability than the conventional CCTA features at assessing coronary plaque vulnerability.KEY POINTS:• CCTA has great potential in the diagnosis of vulnerable coronary artery lesions. • Radiomics model built through CCTA could discriminate coronary vulnerable lesions in good diagnostic ability. • Radiomics model could improve the ability of vulnerability diagnosis against traditional CCTA method, sensitivity especially.

Dose Reduction of Dynamic Computed Tomography Myocardial Perfusion Imaging by Tube Voltage Change: Investigation in a Swine Model.

Background:It is unclear whether tube voltage influences the measurement of perfusion parameters. The present study sought to evaluate the influence of tube voltage change on myocardial blood flow (MBF) measurements in dynamic computed tomography myocardial perfusion imaging (CTP).Methods and Results:Seven swine [mean weight 55.8 kg ± 1.6 (standard deviation)] underwent rest and stress dynamic CTP with tube voltages of 100 and 70 kV. The image noise, signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR), radiation dose and MBF value were compared. The 70 kV images had higher CT attenuation and higher image noise (27.9 ± 2.4 vs. 21.5 ± 1.9, P < 0.001) than the 100 kV images, resulting in a higher SNR (20.5 ± 1.6 vs. 15.6 ± 1.8, P < 0.001) and CNR (17.6 ± 1.5 vs. 12.4 ± 1.7, P < 0.001). Compared to the use of conventional 100 kV, 70 kV yielded an approximately 64.6% radiation dose reduction while generating comparable MBF values, both at rest (88.3 ± 14.9 ml/100 g/min vs. 85.6 ± 17.4 ml/100 g/min, P = 0.21) and stress (101.4 ± 21.5 ml/100 g/min vs. 99.6 ± 21.4 ml/100 g/min, P = 0.58) states.Conclusion:Dynamic CTP using 70 kV instead of 100 kV does not substantially influence the MBF value but significantly reduces the radiation dose. Additional research is required to investigate the clinical significance of this change.

The Perivascular Fat Attenuation Index Improves the Diagnostic Performance for Functional Coronary Stenosis.

Background: Coronary computed tomography angiography (CCTA) is an established first-line test in the investigation of patients with suspected coronary artery disease (CAD), while the perivascular fat attenuation index (FAI) derived from CT seems to be a feasible and efficient tool for the identification of ischemia. The association between the FAI and lesion-specific ischemia as assessed by fractional flow reserve (FFR) remains unclear. Methods: In a total of 261 patients, 294 vessels were assessed for CCTA stenosis, vessel-specific FAI, lesion-specific FAI, and plaque characteristics. The diagnostic accuracies of each parameter and the combined approach were analyzed via the receiver operating characteristic curve (ROC) with FFR as the reference standard. The determinants of FAI were statistically analyzed. Results: The cutoff values of vessel-specific FAI and lesion-specific FAI scores calculated according to the Youden index were −70.97 and −73.95 HU, respectively. No significant differences were noted between them; however, they exhibited a strong correlation. No significant differences were noted between the area under the curve (AUC) scores of vessel-specific FAI (0.677), lesion-specific FAI (0.665), and CCTA (0.607) (p > 0.05 for all) results. The addition of two FAI measures to the CCTA showed improvements in the discrimination (AUC) and reclassification ability (relative integrated discrimination improvement (IDI) and category-free net reclassification index (NRI)), vessel-specific FAI (AUC, 0.696; NRI, 49.6%; IDI, 5.9%), and lesion-specific FAI scores (AUC, 0.676; NRI, 43.3%; IDI, 5.4%); (p < 0.01 for all). Multivariate analysis revealed that low-attenuation plaque (LAP) volume was an independent predictor of two FAI measures. Conclusion: The combined approach of adding vessel-specific FAI or lesion-specific FAI scores could improve the identification of ischemia compared with CCTA alone. The LAP volume was the independent risk factor for both tools.

Diagnostic Performance of CT FFR With a New Parameter Optimized Computational Fluid Dynamics Algorithm From the CT-FFR-CHINA Trial: Characteristic Analysis of Gray Zone Lesions and Misdiagnosed Lesions.

To assess the diagnostic performance of fractional flow reserve (FFR) derived from coronary computed tomography angiography (CTA) (CT-FFR) obtained by a new computational fluid dynamics (CFD) algorithm to detect ischemia, using FFR as a reference, and analyze the characteristics of "gray zone" and misdiagnosed lesions. This prospective multicenter clinical trial (NCT03692936, https://clinicaltrials.gov/) analyzed 317 patients with coronary stenosis between 30 and 90% in 366 vessels from five centers undergoing CTA and FFR between November 2018 and March 2020. CT-FFR were obtained from a CFD algorithm (Heartcentury Co., Ltd., Beijing, China). Diagnostic performance of CT-FFR and CTA in detecting ischemia was assessed. Coronary atherosclerosis characteristics of gray zone and misdiagnosed lesions were analyzed. Per-vessel sensitivity, specificity and accuracy for CT-FFR and CTA were 89.9, 87.8, 88.8% and 89.3, 35.5, 60.4%, respectively. Accuracy of CT-FFR was 80.0% in gray zone lesions. In gray zone lesions, lumen area and diameter were significantly larger than lesions with FFR < 0.76 (both p < 0.001), lesion length, non-calcified and calcified plaque volume were all significantly higher than non-ischemic lesions (all p < 0.05). In gray zone lesions, Agatston score (OR = 1.009, p = 0.044) was the risk factor of false negative results of CT-FFR. In non-ischemia lesions, coronary stenosis >50% (OR = 2.684, p = 0.03) was the risk factor of false positive results. Lumen area (OR = 0.567, p = 0.02) and diameter (OR = 0.296, p = 0.03) had a significant negative effect on the risk of false positive results of CT-FFR. In conclusion, CT-FFR based on the new parameter-optimized CFD model provides better diagnostic performance for lesion-specific ischemia than CTA. For gray zone lesions, stenosis degree was less than those with FFR < 0.76, and plaque load was heavier than non-ischemic lesions.